Abstract
Objectives
To propose multiparametric MRI-based machine learning models and assess their ability to preoperatively predict rectal adenoma with canceration.
Materials and methods
A total of 53 patients with postoperative pathology confirming rectal adenoma (n = 29) and adenoma with canceration (n = 24) were enrolled in this retrospective study. All patients were divided into a training cohort (n = 42) and a test cohort (n = 11). All patients underwent preoperative pelvic MR examination, including high-resolution T2-weighted imaging (HR-T2WI) and diffusion-weighted imaging (DWI). A total of 1396 radiomics features were extracted from the HR-T2WI and DWI sequences, respectively. The least absolute shrinkage and selection operator (LASSO) was utilized for feature selection from the radiomics feature sets from the HR-T2WI and DWI sequences and from the combined feature set with 2792 radiomics features incorporating two sequences. Five-fold cross-validation and two machine learning algorithms (logistic regression, LR; support vector machine, SVM) were utilized for model construction in the training cohort. The diagnostic performance of the models was evaluated by sensitivity, specificity and area under the curve (AUC) and compared with the Delong’s test.
Results
Ten, 8, and 25 optimal features were selected from 1396 HR-T2WI, 1396 DWI and 2792 combined features, respectively. Three group models were constructed using the selected features from HR-T2WI (ModelT2), DWI (ModelDWI) and the two sequences combined (Modelcombined). Modelcombined showed better prediction performance than ModelT2 and ModelDWI. In Modelcombined, there was no significant difference between the LR and SVM algorithms (p = 0.4795), with AUCs in the test cohort of 0.867 and 0.900, respectively.
Conclusions
Multiparametric MRI-based machine learning models have the potential to predict rectal adenoma with canceration. Compared with ModelT2 and ModelDWI, Modelcombined showed the best performance. Moreover, both LR and SVM have equal excellent performance for model construction.
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Abbreviations
- MRI :
-
Magnetic resonance imaging
- HR-T2WI:
-
High-resolution T2-weighted imaging
- DWI:
-
Diffusion-weighted imaging
- LASSO:
-
Least absolute shrinkage and selection operator
- LR:
-
Logistic regression
- SVM:
-
Support vector machine
- AUC:
-
Area under the curve
- CRC:
-
Colorectal cancer
- EMR:
-
Endoscopic mucosal resection
- ESD:
-
Endoscopic submucosal dissection
- ERC:
-
Early rectal cancer
- MP:
-
Muscularis propria
- TE:
-
Echo time
- TR:
-
Repetition time
- FOV:
-
Field of view
- ROI:
-
Region of interest
- GLCM:
-
Gray-level co-occurrence matrix
- GLRLM:
-
Gray-level run-length matrix
- GLSZM:
-
Gray-level size zone matrix
- GLDM:
-
Gray-level dependence matrix
- NGTDM:
-
Neighbouring gray-tone difference matrix
- ICC:
-
Interclass correlation coefficient
- SMOTE:
-
Synthetic minority over-sampling Technique
- ROC:
-
Receiver operating characteristic
- CEA:
-
Carcinoembryonic antigen
- CA199:
-
Carbohydrate antigen 19–9
- CA724:
-
Carbohydrate antigen 724
- FPR :
-
False-positive rate
- FNR :
-
False-negative rate
- SD:
-
Standard deviation
- Sen :
-
Sensitivity
- Spe :
-
Specificity
- MSE :
-
Mean-square error
- NM:
-
Normal mucosa
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PL: analysis and interpretation of the data, drafting of the manuscript; AL: study conception and design; HL: acquisition of the data; RW: acquisition of the data; GS: critical revision; RZ: analysis and interpretation of the data; and PZ: analysis and interpretation of the data.
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All procedures performed in studies involving human participants were approved by the Ethics Commission of the Shandong Provincial Qianfoshan Hospital of Shandong University.
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Li, P., Song, G., Wu, R. et al. Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration. Magn Reson Mater Phy 34, 707–716 (2021). https://doi.org/10.1007/s10334-021-00915-2
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DOI: https://doi.org/10.1007/s10334-021-00915-2